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Web data retrieval: solving spatial range queries using k-nearest neighbor searches

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Abstract

As Geographic Information Systems (GIS) technologies have evolved, more and more GIS applications and geospatial data are available on the web. Spatial objects in a given query range can be retrieved using spatial range query − one of the most widely used query types in GIS and spatial databases. However, it can be challenging to retrieve these data from various web applications where access to the data is only possible through restrictive web interfaces that support certain types of queries. A typical scenario is the existence of numerous business web sites that provide their branch locations through a limited “nearest location” web interface. For example, a chain restaurant’s web site such as McDonalds can be queried to find some of the closest locations of its branches to the user’s home address. However, even though the site has the location data of all restaurants in, for example, the state of California, it is difficult to retrieve the entire data set efficiently due to its restrictive web interface. Considering that k-Nearest Neighbor (k-NN) search is one of the most popular web interfaces in accessing spatial data on the web, this paper investigates the problem of retrieving geospatial data from the web for a given spatial range query using only k-NN searches. Based on the classification of k-NN interfaces on the web, we propose a set of range query algorithms to completely cover the rectangular shape of the query range (completeness) while minimizing the number of k-NN searches as possible (efficiency). We evaluated the efficiency of the proposed algorithms through statistical analysis and empirical experiments using both synthetic and real data sets.

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Notes

  1. Actually, overestimation introduce a slight overhead in each cost function but it is way lower than the overhead caused by underestimation.

  2. The terms, CDT and DCDT, are used for both the corresponding triangulation algorithms and the data structures that support these algorithms in this chapter.

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Acknowledgements

The authors thank Prof. Petr Vojtěchovský for providing helpful suggestions and Brandon Haenlein for the valuable discussions throughout this work.

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Authors and Affiliations

  1. Department of Mathematics, Statistics and Computer Science, University of Wisconsin-Stout, Menomonie, WI, USA

    Wan D. Bae

  2. College of Information Technology, United Arab Emirates University, Al-Ain, United Arab Emirates

    Shayma Alkobaisi

  3. Department of Computer Science & Information Technology, University of District of Columbia, Washington, DC, USA

    Seon Ho Kim

  4. Department of Computer Science, University of Denver, Denver, CO, USA

    Sada Narayanappa

  5. Department of Computer Science, University of Southern California, Los Angeles, CA, USA

    Cyrus Shahabi

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  1. Wan D. Bae
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Correspondence to Wan D. Bae.

Additional information

The author’s work is supported in part by the National Science Foundation under award numbers IIS-0324955 (ITR), EEC-9529152 (IMSC ERC) and IIS-0238560 (PECASE) and in part by unrestricted cash gifts from Microsoft and Google. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Bae, W.D., Alkobaisi, S., Kim, S.H. et al. Web data retrieval: solving spatial range queries using k-nearest neighbor searches. Geoinformatica 13, 483–514 (2009). https://doi.org/10.1007/s10707-008-0055-2

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